Topic
Chatbot
About: Chatbot is a research topic. Over the lifetime, 2415 publications have been published within this topic receiving 24372 citations. The topic is also known as: IM bot & AI chatbot.
Papers published on a yearly basis
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01 Aug 2016TL;DR: This paper presents DocChat, a novel information retrieval approach for chatbot engines that can leverage unstructured documents, instead of Q-R pairs, to respond to utterances.
Abstract: Most current chatbot engines are designed to reply to user utterances based on existing utterance-response (or Q-R)1 pairs. In this paper, we present DocChat, a novel information retrieval approach for chatbot engines that can leverage unstructured documents, instead of Q-R pairs, to respond to utterances. A learning to rank model with features designed at different levels of granularity is proposed to measure the relevance between utterances and responses directly. We evaluate our proposed approach in both English and Chinese: (i) For English, we evaluate DocChat on WikiQA and QASent, two answer sentence selection tasks, and compare it with state-of-the-art methods. Reasonable improvements and good adaptability are observed. (ii) For Chinese, we compare DocChat with XiaoIce2, a famous chitchat engine in China, and side-by-side evaluation shows that DocChat is a perfect complement for chatbot engines using Q-R pairs as main source of responses.
113 citations
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13 May 2017
TL;DR: Various chatbot design techniques, classification of chatbot and discussion on how the modern chatbots have evolved from simple pattern matching, retrieval based model to modern complex knowledge based models are discussed.
Abstract: A conversational agent also referred to as chatbot is a computer program which tries to generate human like responses during a conversation. Earlier chatbots employed much simpler retrieval based pattern matching design techniques. However, with time a number of new chatbots evolved with an aim to make it more human like and hence to pass the Turing test. Now, most of the chatbots employ generative knowledge based techniques. This paper will discuss about various chatbot design techniques, classification of chatbot and discussion on how the modern chatbots have evolved from simple pattern matching, retrieval based model to modern complex knowledge based models. A table of major conversational agents in chronological order along with their design techniques is also provided at the end of the paper.
108 citations
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21 Apr 2020TL;DR: This work designs, implements and evaluates a chatbot that has self-disclosure features when it performs small talk with people, and finds that chatbot self-Disclosure had a reciprocal effect on promoting deeper participant self- Disclosure that lasted over the study period.
Abstract: Chatbots have great potential to serve as a low-cost, effective tool to support people's self-disclosure. Prior work has shown that reciprocity occurs in human-machine dialog; however, whether reciprocity can be leveraged to promote and sustain deep self-disclosure over time has not been systematically studied. In this work, we design, implement and evaluate a chatbot that has self-disclosure features when it performs small talk with people. We ran a study with 47 participants and divided them into three groups to use different chatting styles of the chatbot for three weeks. We found that chatbot self-disclosure had a reciprocal effect on promoting deeper participant self-disclosure that lasted over the study period, in which the other chat styles without self-disclosure features failed to deliver. Chatbot self-disclosure also had a positive effect on improving participants' perceived intimacy and enjoyment over the study period. Finally, we reflect on the design implications of chatbots where deep self-disclosure is needed over time.
108 citations
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TL;DR: The CSIEC system suggests a naive approach of logical reasoning and inference directly through syntactical and semantic analysis of textual knowledge, which has advantages over the old ELIZA-like keywords matching mechanism.
Abstract: CSIEC (Computer Simulation in Educational Communication) system with newly developed multiple functions for English instruction still focuses on supplying a virtual chatting partner (chatbot), which can chat in English with the English learners anytime anywhere. It generates communicative response according to the user input, the dialogue context, the user's and its own personality knowledge, common sense knowledge, and inference knowledge. All these kinds of knowledge are expressed in the form of NLML, an annotation language for natural language text. These NLMLs can either be automatically obtained through parsing the text, or be easily authored with the help of GUI editors designed by us. So the CSIEC system suggests a naive approach of logical reasoning and inference directly through syntactical and semantic analysis of textual knowledge. This approach has advantages over the old ELIZA-like keywords matching mechanism. The chatting log summarization of free Internet usage within six months demonstrates this advantage. In this paper, we present the system architecture and underlying technologies, and the educational application results.
107 citations
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01 Jul 2021-International Journal of Human-computer Studies \/ International Journal of Man-machine Studies
TL;DR: In this article, the authors present a systematic literature review of text-based chatbots, focusing on how users interact with text-Based Chatbots, and map the relevant themes that are recurrent in the last ten years of research.
Abstract: Over the last ten years there has been a growing interest around text-based chatbots, software applications interacting with humans using natural written language. However, despite the enthusiastic market predictions, ‘conversing’ with this kind of agents seems to raise issues that go beyond their current technological limitations, directly involving the human side of interaction. By adopting a Human-Computer Interaction (HCI) lens, in this article we present a systematic literature review of 83 papers that focus on how users interact with text-based chatbots. We map the relevant themes that are recurrent in the last ten years of research, describing how people experience the chatbot in terms of satisfaction, engagement, and trust, whether and why they accept and use this technology, how they are emotionally involved, what kinds of downsides can be observed in human-chatbot conversations, and how the chatbot is perceived in terms of its humanness. On the basis of these findings, we highlight open issues in current research and propose a number of research opportunities that could be tackled in future years.
104 citations